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Geometric Algorithms for Linear Independent Component Analysis

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If you have a question about this talk, please contact Karsten Borgwardt.

Independent Component Analysis (ICA) is a challenging problem in the areas of statistical signal processing and unsupervised machine learning. This talk studies the problem of linear ICA .

Recently, there has been an increasing interest in using geometric optimisation methods to solve linear ICA problems. Two prominent classes of geometric linear ICA methods, Newton-type methods and Jacobi-type methods, will be discussed. This talk will mainly focus on the development and convergence analysis of the proposed geometric linear ICA methods, both theoretically and numerically.

This talk is part of the Machine Learning @ CUED series.

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